Daisy chain distribution in data centers

ABSTRACT

A method and a system to provide daisy chain distribution in data centers are provided. A node identification module identifies three or more data nodes of a plurality of data nodes. The identification of three or more data nodes indicates that the respective data nodes are to receive a copy of a data file. A connection creation module to, using one or more processors, create communication connections between the three or more data nodes. The communication connections form a daisy chain beginning at a seeder data node of the three or more data nodes and ending at a terminal data node of the three or more data nodes.

RELATED APPLICATIONS

This application is a continuation of and claims the benefit of priorityto U.S. patent application Ser. No. 14/476,582, filed Jun. 22, 2015,which is a continuation of and claims the benefit of priority to U.S.patent application Ser. No. 13/754,618, filed on Jan. 30, 2013, nowissued as U.S. Pat. No. 9,065,810, all of which are hereby incorporatedby reference herein in their entirety.

TECHNICAL FIELD

The present application relates generally to the technical field of datamanagement and, in one specific example, to daisy chain distribution ofdata files in data centers.

BACKGROUND

Data centers store large amounts of data across many different machines.Some machines store copies of data stored at other machines.

In the Apache Hadoop open-source software framework, data is distributedacross several data nodes (e.g., a machine or virtual machine) in aHadoop Distributed File System (HDFS). HDFS is a distributed, scalable,and portable filesystem written in Java for the Hadoop framework. InHDFS, various portions and copies of the data may be stored at theseveral data nodes. FIG. 1 depicts an environment 100 comprising a HDFS102. The HDFS 102 has a data file that is stored in a seeder data node104. The seeder data node 104 may distribute the data file, in whole orin part, to one or more leech data nodes such as data node A 106, datanode B 108, data node C 110, additional data nodes (not shown), and/ordata node N 112. The file may be distributed using a protocol such asthe BitTorrent protocol or the hypertext transfer protocol (HTTP).

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which:

FIG. 1 depicts an environment in which the prior art may be practiced.

FIG. 2 is a block diagram of an example management engine, according tovarious embodiments.

FIG. 3 is a network diagram within which a file may be distributedaccording to an example embodiment.

FIG. 4 is a network diagram in an example embodiment where a data nodehas failed.

FIG. 5 is a further network diagram in the example embodiment where adata node has failed.

FIG. 6 is a flowchart illustrating an example method, according tovarious embodiments.

FIG. 7 is a diagrammatic representation of machine in the example formof a computer system within which a set of instructions, for causing themachine to perform any one or more of the methodologies discussedherein, may be executed.

DETAILED DESCRIPTION

Example methods and systems to distribute data files in a data centerusing a daisy chain technique are described. In the followingdescription, for purposes of explanation, numerous specific details areset forth in order to provide a thorough understanding of exampleembodiments. It will be evident, however, to one skilled in the art thatthe present invention may be practiced without these specific details.

Instead of distributing files in a HDFS data center using the BitTorrentor HTTP protocols, a daisy chain technique is provided. The daisy chaintechnique may allow larger files to be transferred to the data nodesfaster than other techniques. To implement the daisy chain technique,multiple Transmission Control Protocol (TCP) connections are set up inthe form of a serial chain, starting at a seeder node and including eachend node to receive the file. The file is then streamed sequentially,one block at a time, from each data node to the next data node in thechain. During transmission, each data node acts as a T-junction,simultaneously saving the next block to disk and forwarding the previousblock to the next data node in the chain.

If a data node in the chain fails during the transfer, the data nodepreceding the failed data node initiates the recovery process bygenerating a TCP connection with the data node subsequent to the faileddata node. A handshake protocol is used to determine which blocks thesubsequent data node in the chain has or has not yet received. Thepreceding node re-transmits the missing blocks to the subsequent nodeand resumes the transmission. The failed data node, when it is backonline, may re-join the chain at the last position in the chain.

While the systems and methods are described in the context of an HDFSsystem, it is understood that the daisy chain distribution protocol maybe used in other file distribution and management contexts. In somespecific instances, the daisy chain distribution protocol may be used inan HDFS system or another kind of system to distribute index filesacross multiple resources or to distribute operating system (OS) imagesat provisioning.

FIG. 2 is a block diagram of an example management engine 200, accordingto various embodiments. The management engine 200 may be implemented aspart of, or separate from, a Hadoop Distributed File System (HDFS). Themanagement system 200 manages generation of a daisy chain distribution,including the management of failed nodes.

A node identification module 202 is configured to identify a pluralityof data nodes (i.e., more than two) that will receive a particular fileas part of a distribution of the file within the data center. The filemay be initially stored at, for example, the seeder data node 104. Theidentified data nodes may be data nodes A 106, B 108, C 110, . . . and N112 or some subset thereof. In some instances, the particular file maybe a “large file” (e.g. more than 30 gigabytes). The data nodes may beidentified based on file size, operating system, network utilizationmetrics, memory space, or some other property specific to the datamanagement system used in the HDFS or the Hadoop framework.

In some instances, the node identification module 202 may determinewhether to distribute a file using a particular technique of a pluralityof available techniques. Available techniques include, but are notlimited to, daisy chain distribution, HTTP, and BitTorrent distribution.The determination may be based on a number of nodes to which the file isto be distributed. For a small number of nodes, HTTP distribution may befavored while for a large number of nodes, BitTorrent may be favored.Another factor may be the availability of specialized hardware. Forexample, HTTP techniques require a hosting server; BitTorrent techniquesrequire Seeders and Trackers; and the daisy chain technique onlyrequires a seeder data node during recovery. Another consideration maybe overhead. For example, BitTorrent techniques require overhead togenerate torrents and initiate seeding over a period of minutes. Incontrast, the daisy chain technique may require a few milliseconds tocreate TCP connections between data nodes. Scalability may be anotherfactor in selecting a distribution technique. For example, HTTPtechniques are not scalable and BitTorrent techniques are scalable butrequire additional Seeders and Trackers (e.g., overhead). The daisychain technique is more scalable because there is a reduced dependenceon the seeder data node. Other considerations may include failurehandling and recovery, slow node handling, flow control, and errordetection.

If the daisy chain technique is used, the node identification module 202may further determine an order in which the identified data nodes areconnected. The order determination may be based on various factors suchas network utilization metrics, memory usage, and machine-specificproperties of each data node.

A connection creation module 204 is configured to generate TCPconnections between a seeder data node (e.g., seeder data node 104)storing a copy of the file to be distributed and the data nodes thatwill store copies of the file to be distributed in a daisy chainformation. A daisy chain formation is a serial arrangement of the datanodes in a specified order. The connection creation module 204 forms twoTCP connections to each data node: an incoming connection connecting thedata node to the data node from which the file is received and anoutgoing TCP connection to the data node to which the data node istransmitting the file. A seeder data node, which stores the first copyof the file, has only an outgoing connection to the next data node inthe daisy chain. Similarly, a terminal data node of the daisy chain hasonly an incoming connection because there are no further data nodes towhich to distribute the file.

In some embodiments, each data node acts as a T-junction, simultaneouslysaving the file to disk and forwarding the previous block to the nextdata node in the chain. In operation, the file may be divided into oneor more blocks, segments, or packets that are transmitted in orderbetween the data nodes in the daisy chain. During a round oftransmission, each data node receives a next block of the file from aprevious data node in the daisy chain and transmits a previous block ofthe file to a next data node in the daisy chain.

A failure recovery module 206 is configured to detect whether a datanode has failed or is otherwise offline and to perform one or morecorrective actions based on the same. The failure recovery module 206may determine that a particular data node has failed based onnon-receipt of an acknowledgement message from the data node. In TCPconnections, receiving nodes respond to a received block of data bysending an acknowledgment message to the sender of the block of data. Ifa data node has sent a block of data but has not received anacknowledgment message from the receiving data node, the receiving datanode may have failed. A failed data node has not received the block ofdata and does not transmit the block of data to the next data node inthe data chain.

When a data node has failed, the failure recovery module 206 instructsthe data node that immediately precedes the failed data node in thedaisy chain to form a “leapfrog” TCP connection with the data nodeimmediately following the failed data node and initiate a handshakeprotocol. Using the handshake protocol, the preceding data nodeidentifies blocks of data not yet received by the following data node.The preceding data node re-transmits the missing blocks of data to thefollowing data node and resumes the daisy chain transmission.

For the failed data nodes that have been “leapfrogged” during the daisychain transmission, recovery is re-started when the failed data nodesare back online. In some instances, the recovery may fall back to anHTTP or BitTorrent protocol after the daisy chain transmission iscomplete. In other instances, the failed data node may be added to thedaisy chain by forming a TCP connection with the terminal data node ofthe daisy chain.

Flow control of data in the daisy chain may be implemented as a policyin the first data node in the chain. Since the first data node readsdata from the source, it can control the rate at which data is read andthus, control the rate at which data flows in the chain. Slow nodes inthe chain may be detected by the management engine in the node beforethe slow node by examining its buffer size. For slow nodes, recovery maybe handled in the same way as failed data nodes.

FIG. 3 is a network diagram 300 within which a file may be distributedaccording to an example embodiment. In the diagram 300, an HDFS 102 isin electronic communication with the management engine 200. Themanagement engine 200 may be separate from the HDFS 102, partiallyintegrated into the HDFS 102, or included in the HDFS 102. The networkfurther includes a plurality of data nodes that are expected to store afile (e.g., seeder data node 104, data node A 106, data node B 108, anddata node C 110 through to data node N 112). The management engine 200is depicted as being connected to only the seeder data node 104 of thedata nodes. However, in various embodiments, all or a portion of thedata nodes may have a direct or indirect connection to the managementengine 200. In some instances, the file may be provided by the HDFS 102to a first data node, referred to as the seeder data node 104, or may bereceived by the seeder data node 104 from another source.

To form the daisy chain, TCP connections are formed between the datanodes to form a sequential chain of the data nodes. As depicted, aconnection 302 is formed between the seeder data node 104 and data nodeA 104. Another connection 304 is formed between data node A 106 and datanode B 108. A third data connection 306 is formed between data node B108 and data node C 110. Additional connections 308 may be formed withfurther data nodes until a terminal data node, data node N 112 isconnected at the end of the daisy chain. In operation, large files arestreamed sequentially, one block at a time, from the seeder node 104 tothe terminal data node N 112. Each data node can act as a ‘T’ junction,simultaneously saving the next block to disk and forwarding the previousblock to the next node in the daisy chain.

FIG. 4 is a network diagram 400 in an example embodiment where data nodeB 108 has failed, indicated by “X” 402. When data node B fails, thepreceding data node, data node A 106, fails to receive anacknowledgement message. Data node A 106 then forms a TCP connection 404with the following data node, data node C110. Data node begins therecovery process by initiating a handshake protocol to identify theblocks of data that have not yet been received by the following datanode, data node C 110.

FIG. 5 is a further network diagram 500 in the example embodiment wherethe data node B 108 has failed. Data node B 108 may come back onlinebefore the daisy chain transmission is complete. In these instances, thefailed data node may rejoin the daisy chain at the terminal data node N112 by forming a TCP connection 502 with the terminal data node N 112,thus becoming the terminal data node of the daisy chain. Using thehandshake protocol, the data node B may receive missing blocks of datafrom the data node N 112.

FIG. 6 is a flowchart illustrating an example method 600, according tovarious embodiments. The method 600 may be performed, in whole or inpart, by the management engine 200. The method 600 begins by determiningwhether to use the daisy chain protocol in an operation 602. Theoperation 602 may be performed by the HDFS 102. If the determination isto not use the daisy chain protocol, the method proceeds to operation604 where the HTTP or BitTorrent protocol is used to distribute thefile.

If the determination is made to use the daisy chain protocol inoperation 602, the data nodes to receive the data file are identified inan operation 606. Next, TCP connections are generated between theidentified nodes to form a daisy chain in an operation 608. In anoperation 610, the file transfer is performed according to the daisychain protocol. In an operation 612, a determination may be made that anode has failed. If a node has failed, a leapfrog TCP connection isformed in an operation 614. If no nodes have failed, the methodcontinues with operation 612 until the file is distributed to the datanodes within the daisy chain.

Modules, Components and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied (1) on a non-transitorymachine-readable medium or (2) in a transmission signal) orhardware-implemented modules. A hardware-implemented module is tangibleunit capable of performing certain operations and may be configured orarranged in a certain manner. In example embodiments, one or morecomputer systems (e.g., a standalone, client or server computer system)or one or more processors may be configured by software (e.g., anapplication or application portion) as a hardware-implemented modulethat operates to perform certain operations as described herein.

In various embodiments, a hardware-implemented module may be implementedmechanically or electronically. For example, a hardware-implementedmodule may comprise dedicated circuitry or logic that is permanentlyconfigured (e.g., as a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an application-specific integratedcircuit (ASIC)) to perform certain operations. A hardware-implementedmodule may also comprise programmable logic or circuitry (e.g., asencompassed within a general-purpose processor or other programmableprocessor) that is temporarily configured by software to perform certainoperations. It will be appreciated that the decision to implement ahardware-implemented module mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the term “hardware-implemented module” should be understoodto encompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired) or temporarily ortransitorily configured (e.g., programmed) to operate in a certainmanner and/or to perform certain operations described herein.Considering embodiments in which hardware-implemented modules aretemporarily configured (e.g., programmed), each of thehardware-implemented modules need not be configured or instantiated atany one instance in time. For example, where the hardware-implementedmodules comprise a general-purpose processor configured using software,the general-purpose processor may be configured as respective differenthardware-implemented modules at different times. Software mayaccordingly configure a processor, for example, to constitute aparticular hardware-implemented module at one instance of time and toconstitute a different hardware-implemented module at a differentinstance of time.

Hardware-implemented modules can provide information to, and receiveinformation from, other hardware-implemented modules. Accordingly, thedescribed hardware-implemented modules may be regarded as beingcommunicatively coupled. Where multiple of such hardware-implementedmodules exist contemporaneously, communications may be achieved throughsignal transmission (e.g., over appropriate circuits and buses) thatconnect the hardware-implemented modules. In embodiments in whichmultiple hardware-implemented modules are configured or instantiated atdifferent times, communications between such hardware-implementedmodules may be achieved, for example, through the storage and retrievalof information in memory structures to which the multiplehardware-implemented modules have access. For example, onehardware-implemented module may perform an operation, and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware-implemented module may then,at a later time, access the memory device to retrieve and process thestored output. Hardware-implemented modules may also initiatecommunications with input or output devices, and can operate on aresource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods described herein may be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod may be performed by one or processors or processor-implementedmodules. The performance of certain of the operations may be distributedamong the one or more processors, not only residing within a singlemachine, but deployed across a number of machines. In some exampleembodiments, the processor or processors may be located in a singlelocation (e.g., within a home environment, an office environment or as aserver farm), while in other embodiments the processors may bedistributed across a number of locations.

The one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., Application Program Interfaces (APIs).)

Electronic Apparatus and System

Example embodiments may be implemented in digital electronic circuitry,or in computer hardware, firmware, software, or in combinations of them.Example embodiments may be implemented using a computer program product,e.g., a computer program tangibly embodied in an information carrier,e.g., in a machine-readable medium for execution by, or to control theoperation of, data processing apparatus, e.g., a programmable processor,a computer, or multiple computers.

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a module, subroutine,or other unit suitable for use in a computing environment. A computerprogram can be deployed to be executed on one computer or on multiplecomputers at one site or distributed across multiple sites andinterconnected by a communication network.

In example embodiments, operations may be performed by one or moreprogrammable processors executing a computer program to performfunctions by operating on input data and generating output. Methodoperations can also be performed by, and apparatus of exampleembodiments may be implemented as, special purpose logic circuitry,e.g., a field programmable gate array (FPGA) or an application-specificintegrated circuit (ASIC).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it will beappreciated that that both hardware and software architectures requireconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor), or a combinationof permanently and temporarily configured hardware may be a designchoice. Below are set out hardware (e.g., machine) and softwarearchitectures that may be deployed, in various example embodiments.

Example Machine Architecture and Machine-Readable Medium

FIG. 7 is a block diagram of machine in the example form of a computersystem 700 within which instructions, for causing the machine to performany one or more of the methodologies discussed herein, may be executed.In alternative embodiments, the machine operates as a standalone deviceor may be connected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server or aclient machine in server-client network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a tablet PC, a set-top box(STB), a Personal Digital Assistant (PDA), a cellular telephone, a webappliance, a network router, switch or bridge, or any machine capable ofexecuting instructions (sequential or otherwise) that specify actions tobe taken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

The example computer system 700 includes a processor 702 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU) orboth), a main memory 704 and a static memory 706, which communicate witheach other via a bus 708. The computer system 700 may further include avideo display unit 710 (e.g., a liquid crystal display (LCD) or acathode ray tube (CRT)). The computer system 700 also includes analphanumeric input device 712 (e.g., a keyboard or a touch-sensitivedisplay screen), a user interface (UI) navigation device 714 (e.g., amouse), a disk drive unit 716, a signal generation device 718 (e.g., aspeaker) and a network interface device 720.

Machine-Readable Medium

The disk drive unit 716 includes a machine-readable medium 722 on whichis stored one or more sets of instructions and data structures (e.g.,software) 724 embodying or utilized by any one or more of themethodologies or functions described herein. The instructions 724 mayalso reside, completely or at least partially, within the main memory704 and/or within the processor 702 during execution thereof by thecomputer system 700, the main memory 704 and the processor 702 alsoconstituting machine-readable media.

While the machine-readable medium 722 is shown in an example embodimentto be a single medium, the term “machine-readable medium” may include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore instructions or data structures. The term “machine-readable medium”shall also be taken to include any tangible medium that is capable ofstoring, encoding or carrying instructions for execution by the machineand that cause the machine to perform any one or more of themethodologies of the present invention, or that is capable of storing,encoding or carrying data structures utilized by or associated with suchinstructions. The term “machine-readable medium” shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media. Specific examples of machine-readable mediainclude non-volatile memory, including by way of example semiconductormemory devices, e.g., Erasable Programmable Read-Only Memory (EPROM),Electrically Erasable Programmable Read-Only Memory (EEPROM), and flashmemory devices; magnetic disks such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

Transmission Medium

The instructions 724 may further be transmitted or received over acommunications network 726 using a transmission medium. The instructions724 may be transmitted using the network interface device 720 and anyone of a number of well-known transfer protocols (e.g., HTTP). Examplesof communication networks include a local area network (“LAN”), a widearea network (“WAN”), the Internet, mobile telephone networks, Plain OldTelephone (POTS) networks, and wireless data networks (e.g., WiFi andWiMax networks). The term “transmission medium” shall be taken toinclude any intangible medium that is capable of storing, encoding orcarrying instructions for execution by the machine, and includes digitalor analog communications signals or other intangible media to facilitatecommunication of such software.

Although an embodiment has been described with reference to specificexample embodiments, it will be evident that various modifications andchanges may be made to these embodiments without departing from thebroader spirit and scope of the invention. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense. The accompanying drawings that form a parthereof, show by way of illustration, and not of limitation, specificembodiments in which the subject matter may be practiced. Theembodiments illustrated are described in sufficient detail to enablethose skilled in the art to practice the teachings disclosed herein.Other embodiments may be utilized and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. This Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

1. A system comprising: the at least one hardware processor; anon-transitory storage medium storing instructions that are executableusing the at least one hardware processor to cause the system to performoperations including: transmitting blocks of data across a sequence ofdata nodes including three or more data nodes beginning at a seeder datanode of the sequence; and detecting a data node of the sequence with anamount of buffered data that exceeds a threshold value; forming aleapfrog communication connection between the detected data node and anext-but-one data node of the sequence; identifying blocks of data notreceived by the next-but-one data node via the leapfrog communicationconnection; and transmitting the identified blocks of data to thenext-but-one data node via the leapfrog communication connection.
 2. Thesystem of claim 1, the operations further comprising: reading the blocksof data from a source via the seeder node; and controlling the rate atwhich the seeder node reads the blocks of data to control the rate atwhich data flows though the sequence of data nodes.
 3. The system ofclaim 1, the operations further comprising transmitting the blocks ofdata using a hypertext transmission protocol (HTTP) or a BitTorrentprotocol.
 4. The system of claim 1, wherein the three or more data nodesare connected in a serial arrangement.
 5. The system of claim 1, whereineach data node of the sequence of data nodes comprises an incomingcommunication connection and an outgoing communication connection. 6.The system of claim 5, wherein the communication connections comprisetransmission control protocol (TCP) connections.
 7. The system of claim1, wherein each data node of the sequence of data nodes is configured tosimultaneously save each block of data to disk and to forward each blockof data to a next data node of the sequence of data nodes.
 8. The systemof claim 1, wherein a data file is divided into the blocks of data thatare transmitted across the sequence of data nodes.
 9. The system ofclaim 8, wherein the data file is managed by a Hadoop Distributed fileSystem (HDFS).
 10. A method comprising: transmitting blocks of dataacross a sequence of data nodes including three or more data nodesbeginning at a seeder data node of the sequence; and detecting a datanode of the sequence with an amount of buffered data that exceeds athreshold value; forming a leapfrog communication connection between thedetected data node and a next-but-one data node of the sequence;identifying blocks of data not received by the next-but-one data nodevia the leapfrog communication connection; and transmitting theidentified blocks of data to the next-but-one data node via the leapfrogcommunication connection.
 11. The method of claim 10, furthercomprising: reading the blocks of data from a source via the seedernode; and controlling the rate at which the seeder node reads the blocksof data to control the rate at which data flows though the sequence ofdata nodes
 12. The method of claim 10, further comprising transmittingthe blocks of data using a hypertext transmission protocol (HTTP) or aBitTorrent protocol.
 13. The method of claim 10, wherein the three ormore data nodes are connected in a serial arrangement.
 14. The method ofclaim 10, wherein each data node of the sequence of data nodes comprisesan incoming communication connection and an outgoing communicationconnection.
 15. The method of claim 14, wherein the communicationconnections comprise transmission control protocol (TCP) connections.16. The method of claim 10, wherein each data node of the sequence ofdata nodes is configured to simultaneously save each block of data todisk and to forward each block of data to a next data node of thesequence of data nodes.
 17. The method of claim 10, wherein a data fileis divided into the blocks of data that are transmitted across thesequence of data nodes.
 18. The method of claim 17, wherein the datafile is managed by a Hadoop Distributed file System (HDFS).
 19. Anon-transitory storage medium storing instructions that are executableby at least one hardware processor of a machine to cause the machine toperform operations including: transmitting blocks of data across asequence of data nodes including three or more data nodes beginning at aseeder data node of the sequence; and detecting a data node of thesequence with an amount of buffered data that exceeds a threshold value;forming a leapfrog communication connection between the detected datanode and a next-but-one data node of the sequence; identifying blocks ofdata not received by the next-but-one data node via the leapfrogcommunication connection; and transmitting the identified blocks of datato the next-but-one data node via the leapfrog communication connection.20. The storage medium of claim 19, the operations further comprising:reading the blocks of data from a source via the seeder node; andcontrolling the rate at which the seeder node reads the blocks of datato control the rate at which data flows though the sequence of datanodes.